[日常] 基于人脸关键点的人脸对齐实现

水深无声 2024-04-17 05:46 78阅读 0赞

> 本篇文档仅讨论通过人脸关键点在二维平面上进行人脸对齐，不讨论三维相关的算法。  
> 代码中所使用 landmark 依据下图所示：
> 
> ![20190823182203437.jpg][]

### 1. 通过双眼坐标进行旋正 ###

import cv2
    import math
    import numpy as np
    
    def Alignment_1(img,landmark):
    
        if landmark.shape[0]==68: # use left_eye and right_eye location
            x = landmark[36,0] - landmark[45,0]
            y = landmark[36,1] - landmark[45,1]
        elif landmark.shape[0]==5:
            x = landmark[0,0] - landmark[1,0]
            y = landmark[0,1] - landmark[1,1]
    
        if x==0:
            angle = 0
        else: 
            angle = math.atan(y/x)*180/math.pi
    
        center = (img.shape[1]//2, img.shape[0]//2)
    
        RotationMatrix = cv2.getRotationMatrix2D(center, angle, 1)
        new_img = cv2.warpAffine(img,RotationMatrix,(img.shape[1],img.shape[0])) 
    
        RotationMatrix = np.array(RotationMatrix)
        new_landmark = []
        for i in range(landmark.shape[0]):
            pts = []    
            pts.append(RotationMatrix[0,0]*landmark[i,0]+RotationMatrix[0,1]*landmark[i,1]+RotationMatrix[0,2])
            pts.append(RotationMatrix[1,0]*landmark[i,0]+RotationMatrix[1,1]*landmark[i,1]+RotationMatrix[1,2])
            new_landmark.append(pts)
    
        new_landmark = np.array(new_landmark)
    
        return new_img, new_landmark

### 2. 通过矩阵运算求解仿射矩阵进行旋正 ###

import cv2
    import math
    import numpy as np
    
    def Alignment_2(std_img,std_landmark,img,landmark):
        
        # https://matthewearl.github.io/2015/07/28/switching-eds-with-python/
        def Transformation(std_landmark,landmark):
            std_landmark = np.matrix(std_landmark).astype(np.float64)
            landmark = np.matrix(landmark).astype(np.float64)
    
            c1 = np.mean(std_landmark, axis=0)
            c2 = np.mean(landmark, axis=0)
            std_landmark -= c1
            landmark -= c2
    
            s1 = np.std(std_landmark)
            s2 = np.std(landmark)
            std_landmark /= s1
            landmark /= s2 
    
            U, S, Vt = np.linalg.svd(std_landmark.T * landmark)
            R = (U * Vt).T
    
            return np.vstack([np.hstack(((s2 / s1) * R, c2.T - (s2 / s1) * R * c1.T)),np.matrix([0., 0., 1.])])
    
        Trans_Matrix = Transformation(std_landmark,landmark) # Shape: 3 * 3
        Trans_Matrix = Trans_Matrix[:2]
        Trans_Matrix = cv2.invertAffineTransform(Trans_Matrix)
        new_img = cv2.warpAffine(img,Trans_Matrix,(img.shape[1],img.shape[0]))
    
        Trans_Matrix = np.array(Trans_Matrix)
        new_landmark = []
        for i in range(landmark.shape[0]):
            pts = []    
            pts.append(Trans_Matrix[0,0]*landmark[i,0]+Trans_Matrix[0,1]*landmark[i,1]+Trans_Matrix[0,2])
            pts.append(Trans_Matrix[1,0]*landmark[i,0]+Trans_Matrix[1,1]*landmark[i,1]+Trans_Matrix[1,2])
            new_landmark.append(pts)
    
        new_landmark = np.array(new_landmark)
    
        return new_img, new_landmark

### 3. 通过最小二乘法求解仿射矩阵进行旋正 ###

import cv2
    import math
    import numpy as np
    
    def Alignment_3(std_img,std_landmark,img,landmark):
        
        def Transformation(std_landmark,landmark):
            Trans_Matrix = np.float32([[1, 0, 0], [0, 1, 0]])
            n_pts = landmark.shape[0]
            ones = np.ones((n_pts, 1), landmark.dtype)
            landmark_ = np.hstack([landmark, ones])
            std_landmark_ = np.hstack([std_landmark, ones])
    
            A, res, rank, s = np.linalg.lstsq(landmark_, std_landmark_,rcond=-1)
    
            if rank == 3:
                Trans_Matrix = np.float32([
                    [A[0, 0], A[1, 0], A[2, 0]],
                    [A[0, 1], A[1, 1], A[2, 1]]
                ])
            elif rank == 2:
                Trans_Matrix = np.float32([
                    [A[0, 0], A[1, 0], 0],
                    [A[0, 1], A[1, 1], 0]
                ])
    
            return Trans_Matrix
    
        # def Transformation(std_landmark,landmark):
        #     from matlab_cp2tform import get_similarity_transform_for_cv2
        #     return get_similarity_transform_for_cv2(landmark, std_landmark, False)
    
        Trans_Matrix = Transformation(std_landmark,landmark) # Shape: 2 * 3
        new_img = cv2.warpAffine(img,Trans_Matrix,(img.shape[1],img.shape[0]))
    
        Trans_Matrix = np.array(Trans_Matrix)
        new_landmark = []
        for i in range(landmark.shape[0]):
            pts = []    
            pts.append(Trans_Matrix[0,0]*landmark[i,0]+Trans_Matrix[0,1]*landmark[i,1]+Trans_Matrix[0,2])
            pts.append(Trans_Matrix[1,0]*landmark[i,0]+Trans_Matrix[1,1]*landmark[i,1]+Trans_Matrix[1,2])
            new_landmark.append(pts)
    
        new_landmark = np.array(new_landmark)
    
        return new_img, new_landmark

### 4. 总结 ###

**对比三种方式，其运行结果如下所示：**

![20190823181623371.jpg][]![20190823181614663.jpg][]![20190823181606147.jpg][]

**参考资料：**

*  [Switching Eds: Face swapping with Python, dlib, and OpenCV][Switching Eds_ Face swapping with Python_ dlib_ and OpenCV]
 *  [numpy.linalg.lstsq这个是什么意思啊？][numpy.linalg.lstsq]
 *  [新视界-OpenCV教程(12)- 图像的几何变换][-OpenCV_12_-]
 *  [【人脸】人脸检测与对齐python实现][python]
 *  [opencv仿射变换GetAffineTransform的总结][opencv_GetAffineTransform]
 *  [Opencv-Python学习笔记五——图像翻转，平移，仿射及透视 warpAffine][Opencv-Python_ warpAffine]

[20190823182203437.jpg]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/11/8570b77a9abc4f25a7ede4c8eadc95d9.jpg
[20190823181623371.jpg]: https://img-blog.csdnimg.cn/20190823181623371.jpg
[20190823181614663.jpg]: https://img-blog.csdnimg.cn/20190823181614663.jpg
[20190823181606147.jpg]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/11/5a722b1ceb2943f2aac5f2be5cd32734.jpg
[Switching Eds_ Face swapping with Python_ dlib_ and OpenCV]: https://matthewearl.github.io/2015/07/28/switching-eds-with-python/
[numpy.linalg.lstsq]: https://www.zhihu.com/question/40540185
[-OpenCV_12_-]: https://zhuanlan.zhihu.com/p/47273624
[python]: https://zhuanlan.zhihu.com/p/55479744
[opencv_GetAffineTransform]: https://blog.csdn.net/gdut2015go/article/details/46359165
[Opencv-Python_ warpAffine]: https://www.jianshu.com/p/ef67cacf442c